Complex group behavior is an important but little understood area of research
with applications both in biology as well as technology. Modeling these
groups as graphs leads to an easier way to understand behavior but potentially
overly simplifies actual natural behavior. Using golden shiner schools
as an example of a social group, I used simulations to examine how various
graph models affect the behavior of information flow through these networks. I
find that only weighted undirected graphs are able to replicate the information
propagation behavior observed in nature. Furthermore, I find that the many
low weighted edges found in the natural golden shiner networks are crucial for
the type of information propagation observed. Even thresholding out edges
that in total represent just 0.5 percent of the total edge weighting in a graph
creates a large change in information propagation behavior.